Work partially funded by Rhône-Alpes and Saint-Étienne Métropole. August 2012.

1. Combining relations and text in scientific network clustering David COMBE1, Christine LARGERON1, Előd EGYED-ZSIGMOND2, Mathias GÉRY1 1. CNRS, UMR 5516, Laboratoire Hubert Curien, Université de Saint-Étienne, Jean-Monnet, Saint-Étienne, France Email : {david.combe, christine.largeron, mathias.gery}@univ-st-etienne.fr 2. Université de Lyon UMR 5205 CNRS, LIRIS Email : elod.egyed-zsigmond@insa-lyon.fr • Detection of communities in social networks using the relationships between the actors and attributes describing them. • Construction of a dataset with ground truth: a scientific social network in which textual data is associated to each vertex and the classes are known. • Proposition of different scenarios for the detection of communities and evaluation of the performance. Aim of the work Problemstatement Dataset Let G = (V, E) a graph describing a social network. V is the set of the vertices and E is the set of edges such that each vertex vi V is associated with a vector of textual attributes where wij is the tf-idf weight of the term tj in the document di. The attributed graph clustering problem is to build a partition of V such that: there should be many edges within each cluster and relatively few between the clusters; two vertices belonging to the same cluster are more similar in terms of attributes, than two vertices belonging to two different clusters. • Vertices: 99 authors from 2 conferences: SAC’09 & IJCAI’09. • Edges: co-participation network generated from DBLP. • Vertices document: the abstracts and titles of the articles were extracted from the websites of the conferences. • Ground truths partitions: • PS= A  B,C  D (conferences) • PT= A,B C, D (research areas) • PTS = A,B,C,D (sessions) SAC SAC IJCAI IJCAI A A Bioinformatics Bioinformatics B C B C Robots Robots Linearcombination D D Constraints Constraints TS1: Structure-based clustering on attribute weighted graph TS3: Linearcombination TS2: Attribute-based clustering on structural distance Cosine distance matrix Vertices distance matrix Textual distance (cosine) matrix Textual distance (cosine) matrix Shortestpathdistance matrix Combined distances matrix Shortestpathprocessing Weighted-graphs clusteringalgorithm  1- Information network Graph valuedwithtextual distance Graph valuedwithtextual distance Graph valuedwithtextual distance Information network Results Conclusion and perspectives Accuracycriterion • Simplestmethodswith no parameter tend to achieve a good classification. • Scenarios considerdifferent aspects of data and givedifferentresults. • Properties of the produced classes depend on the scenario (production of disconnected classes…). • Weintend to help choosing the best clustering scenario for a givendataset. Hierarchicalagglomerative clustering Hierarchicalagglomerative clustering Workpartiallyfundedby Rhône-Alpes and Saint-Étienne Métropole. August 2012.